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International Conference on Analysis of Images, Social Networks and Texts

AIST 2017: Analysis of Images, Social Networks and Texts pp 3–15Cite as

Automated Detection of Adverse Drug Reactions from Social Media Posts with Machine Learning

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10716))

Abstract

Adverse drug reactions can have serious consequences for patients. Social media is a source of information useful for detecting previously unknown side effects from a drug since users publish valuable information about various aspects of their lives, including health care. Therefore, detection of adverse drug reactions from social media becomes one of the actual tools for pharmacovigilance. In this paper, we focus on identification of adverse drug reactions from user reviews and formulate this problem as a binary classification task. We developed a machine learning classifier with a set of features for resolving this problem. Our feature-rich classifier achieves significant improvements on a benchmark dataset over baseline approaches and convolutional neural networks.

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Notes

  1. 1.

    http://diego.asu.edu/index.php?downloads=yes.

  2. 2.

    https://github.com/Ilseyar/adr_classification.

  3. 3.

    https://www.fda.gov/.

  4. 4.

    http://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/CST/.

  5. 5.

    http://www.askapatient.com/.

  6. 6.

    https://www.dailystrength.org/.

  7. 7.

    https://www.drugs.com/.

  8. 8.

    http://jmcauley.ucsd.edu/data/amazon/.

  9. 9.

    http://www.webmd.com/.

  10. 10.

    https://pypi.python.org/pypi/tweet-preprocessor/0.4.0.

  11. 11.

    http://sideeffects.embl.de/.

  12. 12.

    http://www.consumerhealthvocab.org/.

  13. 13.

    https://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/CST/.

  14. 14.

    http://diego.asu.edu/Publications/ADRClassify.html.

  15. 15.

    https://bitbucket.org/asarker/adrbinaryclassifier/downloads/.

  16. 16.

    http://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/CST/.

  17. 17.

    http://sideeffects.embl.de/.

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Acknowledgments

Work on problem definition and neural networks was carried out by Elena Tutubalina and supported by the Russian Science Foundation grant no. 15-11-10019. Other parts of this work were performed according to the Russian Government Program of Competitive Growth of Kazan Federal University.

Author information

Authors and Affiliations

  1. Laboratory of Chemoinformatics and Molecular Modeling, Kazan (Volga Region) Federal University, Kazan, Russia

    Ilseyar Alimova & Elena Tutubalina

Authors
  1. Ilseyar Alimova
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  2. Elena Tutubalina
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Corresponding author

Correspondence to Ilseyar Alimova .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, The Netherlands

    Wil M.P. van der Aalst

  2. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  3. Krasovsky Institute of Mathematics and Mechanics, Ekaterinburg, Russia

    Michael Khachay

  4. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  5. Skolkovo Institute of Science and Technology, Moscow, Russia

    Victor Lempitsky

  6. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  7. Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  8. LORIA, Campus Scientifique, Vandœuvre lès Nancy, France

    Amedeo Napoli

  9. University of Hamburg, Hamburg, Germany

    Alexander Panchenko

  10. University of Florida, Gainesville, Florida, USA

    Panos M. Pardalos

  11. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  12. Indiana University, Bloomington, Indiana, USA

    Stanley Wasserman

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Alimova, I., Tutubalina, E. (2018). Automated Detection of Adverse Drug Reactions from Social Media Posts with Machine Learning. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2017. Lecture Notes in Computer Science(), vol 10716. Springer, Cham. https://doi.org/10.1007/978-3-319-73013-4_1

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  • DOI: https://doi.org/10.1007/978-3-319-73013-4_1

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  • Online ISBN: 978-3-319-73013-4

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